B

Sports Broadcast Rights Price Tracker

2.50

Derivation Chain

Step 1 Surging Korean demand for overseas football and MLB broadcasts
→
Step 2 Intensifying OTT sports broadcast rights competition
→
Step 3 Broadcast rights market price intelligence

Competitor references

No usable source links are available for this idea. Its signal notes and scores need independent verification.

Problem

When Korean OTT operators such as Coupang Play, Tving, and SPOTV acquire overseas sports broadcast rights, there is no systematic data source for tracking league-by-league and season-by-season price trends or competitor contract status. Negotiation preparation takes 2–4 weeks and ₩30+ million (~$22,500+) in external research costs. Price volatility has increased as demand for European league broadcasts surges due to the 'Oh Hyeon-gyu/Son Heung-min effect.'

Solution

Automatically collects and analyzes global sports broadcast rights contract data, providing a dashboard of per-league and per-region price trends along with competitor monitoring alerts. Core features: (1) Broadcast rights transaction price trend database by league/team, (2) Real-time monitoring of competing OTT broadcast schedules, (3) Auto-generated benchmark reports for negotiations.

Target: OTT Platform content strategy teams and sports marketing agencies, 20–200 employees, business development managers aged 30s–40s
Revenue Model: SaaS Monthly Subscription ₩990,000/team (~$743) (5 accounts), 20% discount for annual billing, custom Report ₩2,000,000 (~$1,500) Per Transaction
Ecosystem Role: Infrastructure
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
4.0/5
U Urgency
2.0/5
M Market
2.0/5
R Realizability
2.0/5
V Validation
3.0/5
NUMR-V Scoring System
N Novelty1-5How uncommon the service is in market context.
U Urgency1-5How urgently users need this problem solved now.
M Market1-5Market size and growth potential from proxy indicators.
R Realizability1-5Buildability for a small team with realistic constraints.
V Validation1-5Validation signal quality from competition and demand data.
Each dimension is scored 1-5 and weighted into grade S/A/B/C/D.
N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (71%)

Tech Complexity
29.3/40
Data Availability
22.1/25
MVP Timeline
20.0/20
API Bonus
0.0/15
Feasibility Breakdown
Tech Complexity/ 40Difficulty of core implementation stack.
Data Availability/ 25Practical availability and cost of required data.
MVP Timeline/ 20Expected time to ship a usable MVP.
API Bonus/ 15Bonus for viable public API leverage.
Total 100 points; below 50% is filtered out.

Market Validation (51/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
7.5/15
Pick-Axe Fit
10.5/15
Solo Buildability
5.0/10
Validation Breakdown
Competition/ 20Signal quality from competitor landscape.
Market Demand/ 20Demand proxies from search and mention patterns.
Timing/ 20Fit with current shifts in tech, behavior, and regulation.
Revenue Signals/ 15Reference evidence for monetization viability.
Pick-Axe Fit/ 15How well the concept serves participants in a trend.
Solo Buildability/ 10Practicality for lean-team implementation.
Total 100 points; below 50 is filtered out.

Technical Requirements

Data Pipeline [medium] Backend [medium] Frontend [low]
Dashboard